
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase     :	Optional[Any]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[str]       			=  '▁'

__lowerCAmelCase     :	List[Any]       			=  {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase     :	Optional[Any]       			=  {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__lowerCAmelCase     :	List[Any]       			=  {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__lowerCAmelCase     :	List[Any]       			=  ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						PRETRAINED_VOCAB_FILES_MAP
  a__       			=						["""input_ids""", """attention_mask"""]

  a__       			=						[]
  a__       			=						[]
  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[Any]="<s>"     ,				UpperCamelCase__     :					List[str]="</s>"     ,				UpperCamelCase__     :					int="</s>"     ,				UpperCamelCase__     :					Dict="<s>"     ,				UpperCamelCase__     :					Optional[int]="<unk>"     ,				UpperCamelCase__     :					Optional[Any]="<pad>"     ,				UpperCamelCase__     :					Optional[Any]="<mask>"     ,				UpperCamelCase__     :					Tuple=None     ,				UpperCamelCase__     :					int=None     ,				UpperCamelCase__     :					Union[str, Any]=None     ,				UpperCamelCase__     :					Optional[Dict[str, Any]] = None     ,				UpperCamelCase__     :					Union[str, Any]=None     ,				**UpperCamelCase__     :					List[Any]     ,				)     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else mask_token

    __magic_name__								=			{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=UpperCamelCase__     ,				eos_token=UpperCamelCase__     ,				unk_token=UpperCamelCase__     ,				sep_token=UpperCamelCase__     ,				cls_token=UpperCamelCase__     ,				pad_token=UpperCamelCase__     ,				mask_token=UpperCamelCase__     ,				tokenizer_file=UpperCamelCase__     ,				src_lang=UpperCamelCase__     ,				tgt_lang=UpperCamelCase__     ,				additional_special_tokens=UpperCamelCase__     ,				sp_model_kwargs=self.sp_model_kwargs     ,				**UpperCamelCase__     ,				)

    __magic_name__								=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.Load(str(UpperCamelCase__       )       )
    __magic_name__								=			vocab_file

    # Original fairseq vocab and spm vocab must be "aligned":
    # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
    # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
    # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
    # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

    # Mimic fairseq token-to-id alignment for the first 4 token
    __magic_name__								=			{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

    # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
    __magic_name__								=			1

    __magic_name__								=			len(self.sp_model       )
    __magic_name__								=			{
        code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__       )
    }
    __magic_name__								=			{v: k for k, v in self.lang_code_to_id.items()}
    __magic_name__								=			len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

    self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
    __magic_name__								=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
    __magic_name__								=			list(self.lang_code_to_id.keys()       )

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      self._additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in self._additional_special_tokens]       )

    __magic_name__								=			src_lang if src_lang is not None else """en_XX"""
    __magic_name__								=			self.lang_code_to_id[self._src_lang]
    __magic_name__								=			tgt_lang
    self.set_src_lang_special_tokens(self._src_lang       )
  def __getstate__(					self     :					Union[str, Any]       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.__dict__.copy()
    __magic_name__								=			None
    __magic_name__								=			self.sp_model.serialized_model_proto()
    return state
  def __setstate__(					self     :					Optional[int]     ,				UpperCamelCase__     :					List[str]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			d

    # for backward compatibility
    if not hasattr(self     ,				"""sp_model_kwargs"""       ):
      __magic_name__								=			{}

    __magic_name__								=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )
  @property
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token
  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    return self._src_lang
  @src_lang.setter
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str       )     ->      None:







    """simple docstring"""






    __magic_name__								=			new_src_lang
    self.set_src_lang_special_tokens(self._src_lang       )
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None     ,				UpperCamelCase__     :					bool = False       )     ->      List[int]:







    """simple docstring"""






    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__     ,				token_ids_a=UpperCamelCase__     ,				already_has_special_tokens=UpperCamelCase__       )

    __magic_name__								=			[1] * len(self.prefix_tokens       )
    __magic_name__								=			[1] * len(self.suffix_tokens       )
    if token_ids_a is None:
      return prefix_ones + ([0] * len(UpperCamelCase__       )) + suffix_ones
    return prefix_ones + ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    __magic_name__								=			[self.sep_token_id]
    __magic_name__								=			[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[str]     ,				UpperCamelCase__     :					Optional[str]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      int:







    """simple docstring"""






    if src_lang is None or tgt_lang is None:
      raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
    __magic_name__								=			src_lang
    __magic_name__								=			self(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__       )
    __magic_name__								=			self.convert_tokens_to_ids(UpperCamelCase__       )
    __magic_name__								=			tgt_lang_id
    return inputs
  def 				_lowercase				(					self     :					Any       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			{self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
    vocab.update(self.added_tokens_encoder       )
    return vocab
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    return self.sp_model.encode(UpperCamelCase__     ,				out_type=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Optional[int]       )     ->      Optional[int]:







    """simple docstring"""






    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    __magic_name__								=			self.sp_model.PieceToId(UpperCamelCase__       )

    # Need to return unknown token if the SP model returned 0
    return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    if index in self.fairseq_ids_to_tokens:
      return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					List[str]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			"""""".join(UpperCamelCase__       ).replace(UpperCamelCase__     ,				""" """       ).strip()
    return out_string
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[str] = None       )     ->      Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__       ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
      return
    __magic_name__								=			os.path.join(
        UpperCamelCase__     ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

    if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase__       ) and os.path.isfile(self.vocab_file       ):
      copyfile(self.vocab_file     ,				UpperCamelCase__       )
    elif not os.path.isfile(self.vocab_file       ):
      with open(UpperCamelCase__     ,				"""wb"""       ) as fi:
        __magic_name__								=			self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase__       )

    return (out_vocab_file,)
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str = "en_XX"     ,				UpperCamelCase__     :					Optional[List[str]] = None     ,				UpperCamelCase__     :					str = "ro_RO"     ,				**UpperCamelCase__     :					List[Any]     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			src_lang
    __magic_name__								=			tgt_lang
    return super().prepare_seqaseq_batch(UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      str:







    """simple docstring"""






    return self.set_src_lang_special_tokens(self.src_lang       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    return self.set_tgt_lang_special_tokens(self.tgt_lang       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Any       )     ->      None:







    """simple docstring"""






    __magic_name__								=			self.lang_code_to_id[src_lang]
    __magic_name__								=			[]
    __magic_name__								=			[self.eos_token_id, self.cur_lang_code]





  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					str       )     ->      None:







    """simple docstring"""






    __magic_name__								=			self.lang_code_to_id[lang]
    __magic_name__								=			[]
    __magic_name__								=			[self.eos_token_id, self.cur_lang_code]



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

